1. Field of the Invention
Embodiments presented herein disclose a loitering detection module for a behavioral recognition-based video surveillance system. More specifically, embodiments relate to techniques for detecting loitering behavior of an observed object within a series of video frames.
2. Description of the Related Art
Some currently available video surveillance systems provide simple object recognition capabilities. For example, a video surveillance system may be configured to classify a group of pixels (referred to as a “blob”) in a given frame as being a particular object (e.g., a person or vehicle). Once identified, a “blob” may be tracked from frame-to-frame in order to follow the “blob” moving through the scene over time, e.g., a person walking across the field of vision of a video surveillance camera. Further, such systems may be configured to determine when an object has engaged in certain predefined behaviors. For example, the system may include definitions used to recognize the occurrence of a number of predefined events, e.g., the system may evaluate the appearance of an object classified as depicting a car (a vehicle-appear event) coming to a stop over a number of frames (a vehicle-stop event). Thereafter, a new foreground object may appear and be classified as a person (a person-appear event) and the person then walks out of frame (a person-disappear event). Further, the system may be able to recognize the combination of the first two events as a “parking-event.”
However, such surveillance systems typically require that the objects and/or behaviors which may be recognized by the system be defined in advance. Thus, in practice, these systems rely on predefined definitions for objects and/or behaviors to evaluate a video sequence. Unless the underlying system includes a description for a particular object or behavior, the system is generally incapable of recognizing that behavior (or at least instances of the pattern describing the particular object or behavior). More generally, such systems rely on predefined rules and static patterns and are thus often unable to dynamically identify objects, events, behaviors, or patterns, much less even classify them as either normal or anomalous. Further, such rules-based systems are often unable to identify related anomalies from different patterns of observed behavior, such as loitering behavior of an object within a series of video frames. Loitering detection is a difficult problem for these systems because there are many ways and no set path for an object to loiter. Thus, currently available video surveillance systems have limited usefulness.